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Evolution of microbiomes and the evolution of the study and politics of microbiomes (or, how can something be both ridiculously overhyped and horrifically under-appreciated)

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Talk by Jonathan Eisen for the Microbiome Virtual International Forum December 7, 2021 (PST)

Evolution of microbiomes and the evolution of the study and politics of microbiomes (or, how can something be both ridiculously overhyped and horrifically under-appreciated)

  1. 1. Evolution of microbiomes and the evolution of the study and politics of microbiomes (or, how can something be both ridiculously overhyped and horrifically under-appreciated). Microbiome Virtual International Forum December 7, 2021 (PST) Jonathan A. Eisen University of California, Davis @phylogenomics http://phylogenomics.me
  2. 2. Google Trends Hits to Microbiome The Rise of the Microbiome (2016)
  3. 3. The Rise of the Microbiome (2016) 0 500 1000 1500 2000 2500 3000 3500 4000 4500 1 9 5 6 1 9 5 8 1 9 6 1 1 9 6 3 1 9 6 4 1 9 6 5 1 9 6 6 1 9 6 7 1 9 6 8 1 9 6 9 1 9 7 0 1 9 7 1 1 9 7 2 1 9 7 4 1 9 7 5 1 9 7 6 1 9 7 7 1 9 7 8 1 9 7 9 1 9 8 0 1 9 8 1 1 9 8 2 1 9 8 3 1 9 8 4 1 9 8 5 1 9 8 6 1 9 8 7 1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 Pubmed Hits to Microbiome vs. Year
  4. 4. Why Now I: Appreciation of Microbial Diversity
  5. 5. Why Now II: Post Genome Blues The Microbiome Transcriptome Variome Epigenome Overselling the Human Genome?
  6. 6. Why Now III: Technological Advances
  7. 7. Why Now III: Technological Advances
  8. 8. Why Now IV: Microbiome Functions Turnbaugh et al Nature. 2006 444(7122):1027-31.
  9. 9. Why Now IV: Microbiome Functions Turnbaugh et al Nature. 2006 444(7122):1027-31. #1: Microbiome impacts key trait #2: Microbiome is transferable / modifiable
  10. 10. Why Now V: Importance of Other Microbiomes
  11. 11. Eisen Lab • Rules
  12. 12. Phylogenomics and Evolvability •Mutation •Duplication •Deletion •Rearrangement •Recombination Intrinsic Novelty Origin Evolvability: variation in these processes w/in & between taxa Phylogenomics: integrating genomics & evolution, helps interpret / predict evolvability
  13. 13. •Mutation •Duplication •Deletion •Rearrangement •Recombination Intrinsic Extrinsic Novelty Origin Evolvability & Phylogenomics of Extrinsic Novelties Phylogenomics and Evolvability •Recombination •Gene transfer
  14. 14. •Mutation •Duplication •Deletion •Rearrangement •Recombination Intrinsic •Symbiosis •Symbioses •Microbiomes Extrinsic Novelty Origin Evolvability & Phylogenomics of Extrinsic Novelties Phylogenomics and Evolvability •Recombination •Gene transfer
  15. 15. Eisen Lab “Topics” Phylogenomic Methods & Tools Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Research Projects
  16. 16. Eisen Lab “Topics” Phylogenomic Methods & Tools Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Research Projects Microbial Phylogenomics & Evolvability A Brief Tour of Projects
  17. 17. Phylogenomic Methods & Tools Extrinsic: Symbiosis Symbioses Communities Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Research Projects Area 2: Extrinsic Novelty E2 Extrinsic
  18. 18. Host Microbe Stress (HMS) Triangle Host Microbe Stress E2 Extrinsic
  19. 19. Host Microbiome Stress Host Microbe Stress (HMS) Triangle E2 Extrinsic
  20. 20. Symbiosis Under Stress When organisms are placed under selective pressure or stress where novelty would be beneficial, can we predict which pathway they will use? What leads to interactions / symbioses being a potential solution? Can we manipulate interactions and/or force new ones upon systems? Extrinsic Novelty
  21. 21. HMS Type 1: Nutrient Acquisition Host Microbiome Nutrients E2 Extrinsic
  22. 22. HMS Type 1: Chemosymbioses Marine Invertebrates Endosymbionts Carbon Colleen Cavanaugh E2 Extrinsic Eisen JA, et al.. 1992. Phylogenetic relationships of chemoautotrophic bacterial symbionts of Solemya velum Say (Mollusca: Bivalvia) determined by 16S rRNA gene sequence analysis. Journal of Bacteriology 174: 3416-3421. PMID: 1577710. PMCID: PMC206016. Newton ILG, et al 2007. The Calyptogena magnifica chemoautotrophic symbiont genome. Science 315: 998-1000 Dmytrenko O, et al. 2014. The genome of the intracellular bacterium of the coastal bivalve, Solemya velum: a blueprint for thriving in and out of symbiosis. BMC Genomics 15: 924. Roeselers G, et al.. 2010. Complete genome sequence of Candidatus Ruthia magnifica.
  23. 23. HMS Type 1: Xylem Feeders Glassy Winged Sharpshooter Gut Endosymbionts Trying to Live on Xylem Fluid Nancy Moran Dongying Wu E2 Extrinsic Wu D, Daugherty SC, Van Aken SE, Pai GH, Watkins KL, Khouri H, et al. (2006) Metabolic Complementarity and Genomics of the Dual Bacterial Symbiosis of Sharpshooters. PLoS Biol 4(6): e188. https://doi.org/10.1371/journal.pbio.0040188
  24. 24. HMS Type 1: Nitrogen Acquisition Oloton Corn Mucilage Microbiome Low N Van Deynze A, Zamora P, Delaux PM, Heitmann C, Jayaraman D, Rajasekar S, Graham D, Maeda J, Gibson D, Schwartz KD, Berry AM, Bhatnagar S, Jospin G, Darling A, Jeannotte R, Lopez J, Weimer BC, Eisen JA, Shapiro HY, Ané JM, Bennett AB. 2018. Nitrogen fixation in a landrace of maize is supported by a mucilage-associated diazotrophic microbiota. PLoS Biology 16(8):e2006352. doi: 10.1371/journal.pbio.2006352. PMID: 30086128. PMCID: PMC6080747. E2 Extrinsic
  25. 25. HMS Type 1: Nutrients and Odor Host Microbiome Nutrients Yamaguchi MS, Ganz HH, Cho AW, Zaw TH, Jospin G, McCartney MM, et al. (2019) Bacteria isolated from Bengal cat (Felis catus × Prionailurus bengalensis) anal sac secretions produce volatile compounds potentially associated with animal signaling. PLoS ONE 14(9): e0216846. https://doi.org/10.1371/journal.pone.0216846
  26. 26. HMS Type 1: Nutrients and Odor Host Microbiome Nutrients Yamaguchi MS, Ganz HH, Cho AW, Zaw TH, Jospin G, McCartney MM, et al. (2019) Bacteria isolated from Bengal cat (Felis catus × Prionailurus bengalensis) anal sac secretions produce volatile compounds potentially associated with animal signaling. PLoS ONE 14(9): e0216846. https://doi.org/10.1371/journal.pone.0216846
  27. 27. HMS Type 2: Pathogens Host Microbiome Pathogen E2 Extrinsic
  28. 28. HMS Type 2: Flu & Ducks Ducks Gut Microbiome Flu Walter Boyce Holly Ganz Sarah Hird Ladan Daroud Alana Firl Hird SM, Ganz H, Eisen JA, Boyce WM. 2018. The cloacal microbiome of five wild duck species varies by species and influenza A virus infection status. mSphere 3:e00382-18. https:// doi.org/10.1128/mSphere.00382-18 Ganz, H.H., Doroud, L., Firl, A.J., Hird, S.M., Eisen, J.A. and Boyce, W.M., 2017. Community-level differences in the microbiome of healthy wild mallards and those infected by influenza A viruses. mSystems, 2(1) .e00188-16. E2 Extrinsic
  29. 29. HMS Type 2: Koalas & Chlamydia Koala Gut Microbiome Chlamydia & Antibiotics Katherine Dahlhausen E2 Extrinsic Dahlhausen KE, Doroud L, Firl AJ, Polkinghorne A, Eisen JA. 2018. Characterization of shifts of koala (Phascolarctos cinereus) intestinal microbial communities associated with antibiotic treatment. PeerJ 6:e4452 https://doi.org/ 10.7717/peerj.4452 Dahlhausen KE, Jospin G, Coil DA, Eisen JA, Wilkins LGE. 2020. Isolation and sequence-based characterization of a koala symbiont: Lonepinella koalarum. PeerJ 8:e10177 https://doi.org/10.7717/peerj.10177
  30. 30. Frogs Skin Microbiome Chytrid Sonia Ghose Marina De León HMS Type 2: Frogs and Chytrids E2 Extrinsic
  31. 31. Host Microbiome Changing Environment HMS Type 3: Environmental Change E2 Extrinsic
  32. 32. HMS Type 3: Rice Microbiome Rice Root Microbiome Domestication E2 Extrinsic Sundar Lab Srijak Bhatnagar Edwards J, Johnson C, Santos-Medellin C, Lurie E, Podishetty NK, Bhatnagar S, Eisen JA, Sundaresan V. 2015. Structure, variation, and assembly of the root-associated microbiomes of rice. Proceedings of the National Academy of Sciences USA 12(8): E911-20.
  33. 33. Seagrass Microbiome Returning to The Sea HMS Type 3: Seagrass Land to Sea Jenna Lang Jessica Green Jay Stachowicz David Coil E2 Extrinsic https://seagrassmicrobiome.org
  34. 34. Seagrass Microbiome Returning to The Sea HMS Type 3: Seagrass Land to Sea Jenna Lang Jessica Green Jay Stachowicz David Coil E2 Extrinsic https://seagrassmicrobiome.org Jay Stachowicz Maggie Sogin Gina Chaput
  35. 35. HMS Type 3: Panamanian Isthmus 1000s of Species Microbiome Rise of Panamanian Isthmus Laetitia Wilkins Bill Wcislo Matt Leray E2 Extrinsic https://istmobiome.rbind.io https://istmobiome.net · This work is funded by a grant from the Gordon and Betty Moore Foundation doi:10.37807/GBMF5603 Jarrod Scott David Coil
  36. 36. Phylogenomic Methods & Tools Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Research Projects Microbial Phylogenomics & Evolvability Phylogenomic Methods and Tools A Brief Tour of Methods
  37. 37. Tools: rRNA Phylogeny Driven Methods rRNA Phylogeny Driven Methods Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  38. 38. Eisen et al. 1992 Eisen et al. 1992. J. Bact.174: 3416 Colleen Cavanaugh Chemosynthetic Symbioses
  39. 39. Phylogeny As a Tool in rRNA Analysis Similarity ≠ Relatedness
  40. 40. STAP An Automated Phylogenetic Tree-Based Small Subunit rRNA Taxonomy and Alignment Pipeline (STAP) Dongying Wu1 *, Amber Hartman1,6 , Naomi Ward4,5 , Jonathan A. Eisen1,2,3 1 UC Davis Genome Center, University of California Davis, Davis, California, United States of America, 2 Section of Evolution and Ecology, College of Biological Sciences, University of California Davis, Davis, California, United States of America, 3 Department of Medical Microbiology and Immunology, School of Medicine, University of California Davis, Davis, California, United States of America, 4 Department of Molecular Biology, University of Wyoming, Laramie, Wyoming, United States of America, 5 Center of Marine Biotechnology, Baltimore, Maryland, United States of America, 6 The Johns Hopkins University, Department of Biology, Baltimore, Maryland, United States of America Abstract Comparative analysis of small-subunit ribosomal RNA (ss-rRNA) gene sequences forms the basis for much of what we know about the phylogenetic diversity of both cultured and uncultured microorganisms. As sequencing costs continue to decline and throughput increases, sequences of ss-rRNA genes are being obtained at an ever-increasing rate. This increasing flow of data has opened many new windows into microbial diversity and evolution, and at the same time has created significant methodological challenges. Those processes which commonly require time-consuming human intervention, such as the preparation of multiple sequence alignments, simply cannot keep up with the flood of incoming data. Fully automated methods of analysis are needed. Notably, existing automated methods avoid one or more steps that, though computationally costly or difficult, we consider to be important. In particular, we regard both the building of multiple sequence alignments and the performance of high quality phylogenetic analysis to be necessary. We describe here our fully- automated ss-rRNA taxonomy and alignment pipeline (STAP). It generates both high-quality multiple sequence alignments and phylogenetic trees, and thus can be used for multiple purposes including phylogenetically-based taxonomic assignments and analysis of species diversity in environmental samples. The pipeline combines publicly-available packages (PHYML, BLASTN and CLUSTALW) with our automatic alignment, masking, and tree-parsing programs. Most importantly, this automated process yields results comparable to those achievable by manual analysis, yet offers speed and capacity that are unattainable by manual efforts. Citation: Wu D, Hartman A, Ward N, Eisen JA (2008) An Automated Phylogenetic Tree-Based Small Subunit rRNA Taxonomy and Alignment Pipeline (STAP). PLoS ONE 3(7): e2566. doi:10.1371/journal.pone.0002566 multiple alignment and phylogeny was deemed unfeasible. However, this we believe can compromise the value of the results. For example, the delineation of OTUs has also been automated via tools that do not make use of alignments or phylogenetic trees (e.g., Greengenes). This is usually done by carrying out pairwise comparisons of sequences and then clustering of sequences that have better than some cutoff threshold of similarity with each other). This approach can be powerful (and reasonably efficient) but it too has limitations. In particular, since multiple sequence alignments are not used, one cannot carry out standard phylogenetic analyses. In addition, without multiple sequence alignments one might end up comparing and contrasting different regions of a sequence depending on what it is paired with. The limitations of avoiding multiple sequence alignments and phylogenetic analysis are readily apparent in tools to classify sequences. For example, the Ribosomal Database Project’s Classifier program [29] focuses on composition characteristics of each sequence (e.g., oligonucleotide frequency) and assigns taxonomy based upon clustering genes by their composition. Though this is fast and completely automatable, it can be misled in cases where distantly related sequences have converged on similar composition, something known to be a major problem in ss-rRNA sequences [30]. Other taxonomy assignment systems focus classification tools it does have some limitations. For example, the generation of new alignments for each sequence is both computational costly, and does not take advantage of available curated alignments that make use of ss-RNA secondary structure to guide the primary sequence alignment. Perhaps most importantly however is that the tool is not fully automated. In addition, it does not generate multiple sequence alignments for all sequences in a dataset which would be necessary for doing many analyses. Automated methods for analyzing rRNA sequences are also available at the web sites for multiple rRNA centric databases, such as Greengenes and the Ribosomal Database Project (RDPII). Though these and other web sites offer diverse powerful tools, they do have some limitations. For example, not all provide multiple sequence alignments as output and few use phylogenetic approaches for taxonomy assignments or other analyses. More importantly, all provide only web-based interfaces and their integrated software, (e.g., alignment and taxonomy assignment), cannot be locally installed by the user. Therefore, the user cannot take advantage of the speed and computing power of parallel processing such as is available on linux clusters, or locally alter and potentially tailor these programs to their individual computing needs (Table 1). Table 1. Comparison of STAP’s computational abilities relative to existing commonly-used ss-RNA analysis tools. STAP ARB Greengenes RDP Installed where? Locally Locally Web only Web only User interface Command line GUI Web portal Web portal Parallel processing YES NO NO NO Manual curation for taxonomy assignment NO YES NO NO Manual curation for alignment NO YES NO* NO Open source YES** NO NO NO Processing speed Fast Slow Medium Medium It is important to note, that STAP is the only software that runs on the command line and can take advantage of parallel processing on linux clusters and, further, is more amenable to downstream code manipulation. * Note: Greengenes alignment output is compatible with upload into ARB and downstream manual alignment. ** The STAP program itself is open source, the programs it depends on are freely available but not open source. doi:10.1371/journal.pone.0002566.t001 ss-rRNA Taxonomy Pipeline STAP database, and the query sequence is aligned to them using the CLUSTALW profile alignment algorithm [40] as described above for domain assignment. By adapting the profile alignment algorithm, the al while gaps are in sequence accord Figure 1. A flow chart of the STAP pipeline. doi:10.1371/journal.pone.0002566.g001 STAP database, and the query sequence is aligned to them using the CLUSTALW profile alignment algorithm [40] as described above for domain assignment. By adapting the profile alignment algorithm, the alignments from the STAP database remain intact, while gaps are inserted and nucleotides are trimmed for the query sequence according to the profile defined by the previous alignments from the databases. Thus the accuracy and quality of the alignment generated at this step depends heavily on the quality of the Bacterial/Archaeal ss-rRNA alignments from the Greengenes project or the Eukaryotic ss-rRNA alignments from the RDPII project. Phylogenetic analysis using multiple sequence alignments rests on the assumption that the residues (nucleotides or amino acids) at the same position in every sequence in the alignment are homologous. Thus, columns in the alignment for which ‘‘positional homology’’ cannot be robustly determined must be excluded from subsequent analyses. This process of evaluating homology and eliminating questionable columns, known as masking, typically requires time- consuming, skillful, human intervention. We designed an automat- ed masking method for ss-rRNA alignments, thus eliminating this bottleneck in high-throughput processing. First, an alignment score is calculated for each aligned column by a method similar to that used in the CLUSTALX package [42]. Specifically, an R-dimensional sequence space representing all the possible nucleotide character states is defined. Then for each aligned column, the nucleotide populating that column in each of the aligned sequences is assigned a score in each of the R dimensions (Sr) according to the IUB matrix [42]. The consensus ‘‘nucleotide’’ for each column (X) also has R dimensions, with the Figure 2. Domain assignment. In Step 1, STAP assigns a domain to each query sequence based on its position in a maximum likelihood tree of representative ss-rRNA sequences. Because the tree illustrated Figure 1. A flow chart of the STAP pipeline. doi:10.1371/journal.pone.0002566.g001 ss-rRNA Taxonomy Pipeline
  41. 41. WATERS Hartman et al. BMC Bioinformatics 2010, 11:317 http://www.biomedcentral.com/1471-2105/11/317 Open Access SOFTWARE Software Introducing W.A.T.E.R.S.: a Workflow for the Alignment, Taxonomy, and Ecology of Ribosomal Sequences Amber L Hartman†1,3, Sean Riddle†2, Timothy McPhillips2, Bertram Ludäscher2 and Jonathan A Eisen*1 Abstract Background: For more than two decades microbiologists have used a highly conserved microbial gene as a phylogenetic marker for bacteria and archaea. The small-subunit ribosomal RNA gene, also known as 16 S rRNA, is encoded by ribosomal DNA, 16 S rDNA, and has provided a powerful comparative tool to microbial ecologists. Over time, the microbial ecology field has matured from small-scale studies in a select number of environments to massive collections of sequence data that are paired with dozens of corresponding collection variables. As the complexity of data and tool sets have grown, the need for flexible automation and maintenance of the core processes of 16 S rDNA sequence analysis has increased correspondingly. Results: We present WATERS, an integrated approach for 16 S rDNA analysis that bundles a suite of publicly available 16 S rDNA analysis software tools into a single software package. The "toolkit" includes sequence alignment, chimera removal, OTU determination, taxonomy assignment, phylogentic tree construction as well as a host of ecological analysis and visualization tools. WATERS employs a flexible, collection-oriented 'workflow' approach using the open- source Kepler system as a platform. Conclusions: By packaging available software tools into a single automated workflow, WATERS simplifies 16 S rDNA analyses, especially for those without specialized bioinformatics, programming expertise. In addition, WATERS, like some of the newer comprehensive rRNA analysis tools, allows researchers to minimize the time dedicated to carrying out tedious informatics steps and to focus their attention instead on the biological interpretation of the results. One advantage of WATERS over other comprehensive tools is that the use of the Kepler workflow system facilitates result interpretation and reproducibility via a data provenance sub-system. Furthermore, new "actors" can be added to the workflow as desired and we see WATERS as an initial seed for a sizeable and growing repository of interoperable, easy- to-combine tools for asking increasingly complex microbial ecology questions. Background Microbial communities and how they are surveyed Microbial communities abound in nature and are crucial for the success and diversity of ecosystems. There is no end in sight to the number of biological questions that can be asked about microbial diversity on earth. From animal and human guts to open ocean surfaces and deep sea hydrothermal vents, to anaerobic mud swamps or boiling thermal pools, to the tops of the rainforest canopy and the frozen Antarctic tundra, the composition of microbial communities is a source of natural history, intellectual curiosity, and reservoir of environmental health [1]. Microbial communities are also mediators of insight into global warming processes [2,3], agricultural success [4], pathogenicity [5,6], and even human obesity [7,8]. In the mid-1980 s, researchers began to sequence ribo- somal RNAs from environmental samples in order to characterize the types of microbes present in those sam- ples, (e.g., [9,10]). This general approach was revolution- ized by the invention of the polymerase chain reaction (PCR), which made it relatively easy to clone and then * Correspondence: jaeisen@ucdavis.edu 1 Department of Medical Microbiology and Immunology and the Department of Evolution and Ecology, Genome Center, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA † Contributed equally Full list of author information is available at the end of the article 11:317 105/11/317 Page 2 of 14 bosomal RNA) in partic- osomal RNA (ss-rRNA). e amount of previously [1,11-13]. Researchers t rRNA gene not only it can be PCR amplified, e and highly conserved ersally distributed among ful for inferring phyloge- e then, "cultivation-inde- ught a revolution to the ng scientists to study a Align Check chimeras Cluster Build Tree Assign Taxonomy Tree w/ Taxonomy Diversity statistics & graphs Unifrac files Cytoscape network OTU table Hartman et al. BMC Bioinformatics 2010, 11:317 http://www.biomedcentral.com/1471-2105/11/317 Page 3 of 14 Motivations As outlined above, successfully processing microbial sequence collections is far from trivial. Each step is com- plex and usually requires significant bioinformatics expertise and time investment prior to the biological interpretation. In order to both increase efficiency and ensure that all best-practice tools are easily usable, we sought to create an "all-inclusive" method for performing all of these bioinformatics steps together in one package. To this end, we have built an automated, user-friendly, workflow-based system called WATERS: a Workflow for the Alignment, Taxonomy, and Ecology of Ribosomal Sequences (Fig. 1). In addition to being automated and simple to use, because WATERS is executed in the Kepler scientific workflow system (Fig. 2) it also has the advan- tage that it keeps track of the data lineage and provenance of data products [23,24]. Automation The primary motivation in building WATERS was to minimize the technical, bioinformatics challenges that arise when performing DNA sequence clustering, phylo- genetic tree, and statistical analyses by automating the 16 S rDNA analysis workflow. We also hoped to exploit additional features that workflow-based approaches entail, such as optimized execution and data lineage tracking and browsing [23,25-27]. In the earlier days of 16 S rDNA analysis, simply knowing which microbes were present and whether they were biologically novel was a noteworthy achievement. It was reasonable and expected, therefore, to invest a large amount of time and effort to get to that list of microbes. But now that current efforts are significantly more advanced and often require com- parison of dozens of factors and variables with datasets of thousands of sequences, it is not practically feasible to process these large collections "by hand", and hugely inef- ficient if instead automated methods can be successfully employed. Broadening the user base A second motivation and perspective is that by minimiz- ing the technical difficulty of 16 S rDNA analysis through the use of WATERS, we aim to make the analysis of these datasets more widely available and allow individuals with Figure 2 Screenshot of WATERS in Kepler software. Key features: the library of actors un-collapsed and displayed on the left-hand side, the input and output paths where the user declares the location of their input files and desired location for the results files. Each green box is an individual Kepler actor that performs a single action on the data stream. The connectors (black arrows) direct and hook up the actors in a defined sequence. Double- clicking on any actor or connector allows it to be manipulated and re-arranged. Hartman et al. BMC Bioinformatics 2010, 11:317 http://www.biomedcentral.com/1471-2105/11/317 Page 9 default is 97% and 99%), and they are also generated for every metadata variable comparison that the user includes. Data pruning To assist in troubleshooting and quality con WATERS returns to the user three fasta files of seque Figure 3 Biologically similar results automatically produced by WATERS on published colonic microbiota samples. (A) Rarefaction curves ilar to curves shown in Eckburg et al. Fig. 2; 70-72, indicate patient numbers, i.e., 3 different individuals. (B) Weighted Unifrac analysis based on ph genetic tree and OTU data produced by WATERS very similar to Eckburg et al. Fig. 3B. (C) Neighbor-joining phylogenetic tree (Quicktree) represent the sequences analyzed by WATERS, which is clearly similar to Fig. S1 in Eckburg et al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
  42. 42. alignment used to build the profile, resulting in a multiple sequence alignment of full-length reference sequences and metagenomic reads. The final step of the alignment process is a quality control filter that 1) ensures that only homologous SSU- rRNA sequences from the appropriate phylogenetic domain are included in the final alignment, and 2) masks highly gapped alignment columns (see Text S1). We use this high quality alignment of metagenomic reads and references sequences to construct a fully-resolved, phylogenetic tree and hence determine the evolutionary relationships between the reads. Reference sequences are included in this stage of the analysis to guide the phylogenetic assignment of the relatively short metagenomic reads. While the software can be easily extended to incorporate a number of different phylogenetic tools capable of analyzing metagenomic data (e.g., RAxML [27], pplacer [28], etc.), PhylOTU currently employs FastTree as a default method due to its relatively high speed-to-performance PD versus PID clustering, 2) to explore overlap between PhylOTU clusters and recognized taxonomic designations, and 3) to quantify the accuracy of PhylOTU clusters from shotgun reads relative to those obtained from full-length sequences. PhylOTU Clusters Recapitulate PID Clusters We sought to identify how PD-based clustering compares to commonly employed PID-based clustering methods by applying the two methods to the same set of sequences. Both PID-based clustering and PhylOTU may be used to identify OTUs from overlapping sequences. Therefore we applied both methods to a dataset of 508 full-length bacterial SSU-rRNA sequences (refer- ence sequences; see above) obtained from the Ribosomal Database Project (RDP) [25]. Recent work has demonstrated that PID is more accurately calculated from pairwise alignments than multiple sequence alignments [32–33], so we used ESPRIT, which Figure 1. PhylOTU Workflow. Computational processes are represented as squares and databases are represented as cylinders in this generalize workflow of PhylOTU. See Results section for details. doi:10.1371/journal.pcbi.1001061.g001 Finding Metagenomic OTUs Sharpton TJ, Riesenfeld SJ, Kembel SW, Ladau J, O'Dwyer JP, Green JL, Eisen JA, Pollard KS. (2011) PhylOTU: A High- Throughput Procedure Quantifies Microbial Community Diversity and Resolves Novel Taxa from Metagenomic Data. PLoS Comput Biol 7(1): e1001061. doi:10.1371/ journal.pcbi.1001061 OTUs via Phylogeny (PhylOTU) Tom Sharpton Katie Pollard Jessica Green Finding Metagenomic OTUs
  43. 43. rRNA Copy # vs. Phylogeny Steven Kembel Jessica Green Martin
 Wu Kembel SW, Wu M, Eisen JA, Green JL (2012) Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance. PLoS Comput Biol 8(10): e1002743. doi:10.1371/ journal.pcbi.1002743
  44. 44. Other Marker Genes Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems Tools: Other Marker Genes
  45. 45. Metagenomics DNA RecA RecA RecA RpoB RpoB RpoB Rpl4 Rpl4 Rpl4 rRNA rRNA rRNA Hsp70 Hsp70 Hsp70 EFTu EFTu EFTu http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Volume 9, Issue 10, Article R151 Wu and Eisen R151.7 Genome Biology 2008, 9:R151 sequences are not conserved at the nucleotide level [29]. As a result, the nr database does not actually contain many more protein marker sequences that can be used as references than those available from complete genome sequences. Comparison of phylogeny-based and similarity-based phylotyping Although our phylogeny-based phylotyping is fully auto- mated, it still requires many more steps than, and is slower than, similarity based phylotyping methods such as a MEGAN [30]. Is it worth the trouble? Similarity based phylo- typing works by searching a query sequence against a refer- ence database such as NCBI nr and deriving taxonomic information from the best matches or 'hits'. When species that are closely related to the query sequence exist in the ref- erence database, similarity-based phylotyping can work well. However, if the reference database is a biased sample or if it contains no closely related species to the query, then the top hits returned could be misleading [31]. Furthermore, similar- ity-based methods require an arbitrary similarity cut-off value to define the top hits. Because individual bacterial genomes and proteins can evolve at very different rates, a uni- versal cut-off that works under all conditions does not exist. As a result, the final results can be very subjective. In contrast, our tree-based bracketing algorithm places the query sequence within the context of a phylogenetic tree and only assigns it to a taxonomic level if that level has adequate sampling (see Materials and methods [below] for details of the algorithm). With the well sampled species Prochlorococ- cus marinus, for example, our method can distinguish closely related organisms and make taxonomic identifications at the species level. Our reanalysis of the Sargasso Sea data placed 672 sequences (3.6% of the total) within a P. marinus clade. On the other hand, for sparsely sampled clades such as Aquifex, assignments will be made only at the phylum level. Thus, our phylogeny-based analysis is less susceptible to data sampling bias than a similarity based approach, and it makes Major phylotypes identified in Sargasso Sea metagenomic data Figure 3 Major phylotypes identified in Sargasso Sea metagenomic data. The metagenomic data previously obtained from the Sargasso Sea was reanalyzed using AMPHORA and the 31 protein phylogenetic markers. The microbial diversity profiles obtained from individual markers are remarkably consistent. The breakdown of the phylotyping assignments by markers and major taxonomic groups is listed in Additional data file 5. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 A l p h a p r o t e o b a c t e r i a B e t a p r o t e o b a c t e r i a G a m m a p r o t e o b a c t e r i a D e l t a p r o t e o b a c t e r i a E p s i l o n p r o t e o b a c t e r i a U n c l a s s i f i e d p r o t e o b a c t e r i a B a c t e r o i d e t e s C h l a m y d i a e C y a n o b a c t e r i a A c i d o b a c t e r i a T h e r m o t o g a e F u s o b a c t e r i a A c t i n o b a c t e r i a A q u i f i c a e P l a n c t o m y c e t e s S p i r o c h a e t e s F i r m i c u t e s C h l o r o f l e x i C h l o r o b i U n c l a s s i f i e d b a c t e r i a dnaG frr infC nusA pgk pyrG rplA rplB rplC rplD rplE rplF rplK rplL rplM rplN rplP rplS rplT rpmA rpoB rpsB rpsC rpsE rpsI rpsJ rpsK rpsM rpsS smpB tsf Relative abundance Many other genes better than rRNA
  46. 46. Sargasso Phylotypes Weighted % of Clones 0.000 0.125 0.250 0.375 0.500 Major Phylogenetic Group A l p h a p r o t e o b a c t e r i a B e t a p r o t e o b a c t e r i a G a m m a p r o t e o b a c t e r i a E p s i l o n p r o t e o b a c t e r i a D e l t a p r o t e o b a c t e r i a C y a n o b a c t e r i a F i r m i c u t e s A c t i n o b a c t e r i a C h l o r o b i C F B C h l o r o fl e x i S p i r o c h a e t e s F u s o b a c t e r i a D e i n o c o c c u s - T h e r m u s E u r y a r c h a e o t a C r e n a r c h a e o t a EFG EFTu HSP70 RecA RpoB rRNA Venter et al., Science 304: 66. 2004 Marker Phylotyping - Sargasso Metagenome
  47. 47. Amphora W Martin
 Wu
  48. 48. AMPHORA http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Volume 9, Issue 10, Article R151 Wu and Eisen R151.7 Major phylotypes identified in Sargasso Sea metagenomic data Figure 3 Major phylotypes identified in Sargasso Sea metagenomic data. The metagenomic data previously obtained from the Sargasso Sea was reanalyzed using AMPHORA and the 31 protein phylogenetic markers. The microbial diversity profiles obtained from individual markers are remarkably consistent. The 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 A l p h a p r o t e o b a c t e r i a B e t a p r o t e o b a c t e r i a G a m m a p r o t e o b a c t e r i a D e l t a p r o t e o b a c t e r i a E p s i l o n p r o t e o b a c t e r i a U n c l a s s i f i e d p r o t e o b a c t e r i a B a c t e r o i d e t e s C h l a m y d i a e C y a n o b a c t e r i a A c i d o b a c t e r i a T h e r m o t o g a e F u s o b a c t e r i a A c t i n o b a c t e r i a A q u i f i c a e P l a n c t o m y c e t e s S p i r o c h a e t e s F i r m i c u t e s C h l o r o f l e x i C h l o r o b i U n c l a s s i f i e d b a c t e r i a dnaG frr infC nusA pgk pyrG rplA rplB rplC rplD rplE rplF rplK rplL rplM rplN rplP rplS rplT rpmA rpoB rpsB rpsC rpsE rpsI rpsJ rpsK rpsM rpsS smpB tsf Relative abundance AMPHORA Phylotyping w/ Protein Markers Martin
 Wu
  49. 49. Phylosift - Bayesian Phylotyping Input Sequences rRNA workflow protein workflow profile HMMs used to align candidates to reference alignment Taxonomic Summaries parallel option hmmalign multiple alignment LAST fast candidate search pplacer phylogenetic placement LAST fast candidate search LAST fast candidate search search input against references hmmalign multiple alignment hmmalign multiple alignment Infernal multiple alignment LAST fast candidate search <600 bp >600 bp Sample Analysis & Comparison Krona plots, Number of reads placed for each marker gene Edge PCA, Tree visualization, Bayes factor tests each input sequence scanned against both workflows Aaron Darling Erik Matsen Holly Bik Guillaume Jospin Darling AE, Jospin G, Lowe E, Matsen FA IV, Bik HM, Eisen JA. (2014) PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ 2:e243 http://dx.doi.org/10.7717/ peerj.243 Erik Lowe
  50. 50. PD from Metagenomes typically used as a qualitative measure because duplicate s quences are usually removed from the tree. However, the test may be used in a semiquantitative manner if all clone even those with identical or near-identical sequences, are i cluded in the tree (13). Here we describe a quantitative version of UniFrac that w call “weighted UniFrac.” We show that weighted UniFrac b haves similarly to the FST test in situations where both a FIG. 1. Calculation of the unweighted and the weighted UniFr measures. Squares and circles represent sequences from two differe environments. (a) In unweighted UniFrac, the distance between t circle and square communities is calculated as the fraction of t branch length that has descendants from either the square or the circ environment (black) but not both (gray). (b) In weighted UniFra branch lengths are weighted by the relative abundance of sequences the square and circle communities; square sequences are weight twice as much as circle sequences because there are twice as many tot circle sequences in the data set. The width of branches is proportion to the degree to which each branch is weighted in the calculations, an gray branches have no weight. Branches 1 and 2 have heavy weigh since the descendants are biased toward the square and circles, respe tively. Branch 3 contributes no value since it has an equal contributio from circle and square sequences after normalization. Kembel SW, Eisen JA, Pollard KS, Green JL (2011) The Phylogenetic Diversity of Metagenomes. PLoS ONE 6(8): e23214. doi:10.1371/journal.pone.0023214 Jessica Green Steven Kembel Katie Pollard
  51. 51. Tools: Phylogenomic Functional Prediction Phylogenomic Functional Prediction Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  52. 52. Phylogenomic Functional Prediction To understand how functions evolve, We need to be able to predict functions well from sequence data. Tools: Phylogenomic Functional Prediction
  53. 53. PSA Similarity ≠ Relatedness
  54. 54. PHYLOGENENETIC PREDICTION OF GENE FUNCTION IDENTIFY HOMOLOGS OVERLAY KNOWN FUNCTIONS ONTO TREE INFER LIKELY FUNCTION OF GENE(S) OF INTEREST 1 2 3 4 5 6 3 5 3 1A 2A 3A 1B 2B 3B 2A 1B 1A 3A 1B 2B 3B ALIGN SEQUENCES CALCULATE GENE TREE 1 2 4 6 CHOOSE GENE(S) OF INTEREST 2A 2A 5 3 Species 3 Species 1 Species 2 1 1 2 2 2 3 1 1A 3A 1A 2A 3A 1A 2A 3A 4 6 4 5 6 4 5 6 2B 3B 1B 2B 3B 1B 2B 3B ACTUAL EVOLUTION (ASSUMED TO BE UNKNOWN) Duplication? EXAMPLE A EXAMPLE B Duplication? Duplication? Duplication 5 METHOD Ambiguous Based on Eisen, 1998 Genome Res 8: 163-167. Phylogenomics
  55. 55. Phylotyping Eisen et al. 1992 Eisen et al. 1992. J. Bact.174: 3416
  56. 56. Shotmap Simulate) metagenomic) library) Translate) metagenomic) reads) Search) metagenomic) pep6des) Classify) metagenomic) pep6des) Es6mate) protein)family) abundance) Taxonomic) profiles)from)real) metagenomes) Protein)family) database) IMG/ER) reference) genomes) Construct)) mock)) community) 1" Annotate) genes)in) genomes) 2" Expected) abundance)of) gene)families) 3" 4" 5" Protein)family) database) Evaluate) es6ma6on) accuracy) 6" 7" 8" 9" Tom Sharpton Katie Pollard https://github.com/sharpton/shotmap Shotmap
  57. 57. Tools: Phylogenetic Profiling Phylogenetic Profiling Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  58. 58. Sporulation Gene Profile Wu et al. 2005 PLoS Genetics 1: e65.
  59. 59. B. subtilis new sporulation genes Bjorn Traag Richard Losick Antonia Pugliese J Bacteriol. 2013 Jan;195(2):253-60. doi: 10.1128/JB.01778-12
  60. 60. Tools: Whole Genome Phylogeny Whole Genome Phylogeny Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  61. 61. Whole Genome Phylogeny To understand how functions evolve, We need to know how organisms are related to each other Tools: Whole Genome Phylogeny
  62. 62. Automated WGT: Amphora W Martin
 Wu
  63. 63. Automated WGT: Phylosift Input Sequences rRNA workflow protein workflow profile HMMs used to align candidates to reference alignment Taxonomic Summaries parallel option hmmalign multiple alignment LAST fast candidate search pplacer phylogenetic placement LAST fast candidate search LAST fast candidate search search input against references hmmalign multiple alignment hmmalign multiple alignment Infernal multiple alignment LAST fast candidate search <600 bp >600 bp Sample Analysis & Comparison Krona plots, Number of reads placed for each marker gene Edge PCA, Tree visualization, Bayes factor tests each input sequence scanned against both workflows Aaron Darling Erik Matsen Holly Bik Guillaume Jospin Darling AE, Jospin G, Lowe E, Matsen FA IV, Bik HM, Eisen JA. (2014) PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ 2:e243 http://dx.doi.org/10.7717/ peerj.243 Erik Lowe
  64. 64. Normalizing Across Genes Tree OTU Wu, D., Doroud, L, Eisen, JA 2013. arXiv. TreeOTU: Operational Taxonomic Unit Classification Based on Phylogenetic Dongying Wu
  65. 65. Tools: Linking Phylogeny and Function Linking Phylogeny & Function Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  66. 66. Resources and Reference Data Phylogenomic Methods & Tools Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems A Brief Tour of Resources
  67. 67. Phylogeny can guide generation of reference data Resources and Reference Data
  68. 68. Phylogeny Guided Genome Sequencing
  69. 69. MAGs
  70. 70. SFAMs (Sifting Families) Representative Genomes Extract Protein Annotation All v. All BLAST Homology Clustering (MCL) SFams Align & Build HMMs HMMs Screen for Homologs New Genomes Extract Protein Annotation Figure 1 Sharpton et al. 2012.BMC bioinformatics, 13(1), 264. A B C
  71. 71. PhyEco Markers Phylogenetic group Genome Number Gene Number Maker Candidates Archaea 62 145415 106 Actinobacteria 63 267783 136 Alphaproteobacteria 94 347287 121 Betaproteobacteria 56 266362 311 Gammaproteobacteria 126 483632 118 Deltaproteobacteria 25 102115 206 Epislonproteobacteria 18 33416 455 Bacteriodes 25 71531 286 Chlamydae 13 13823 560 Chloroflexi 10 33577 323 Cyanobacteria 36 124080 590 Firmicutes 106 312309 87 Spirochaetes 18 38832 176 Thermi 5 14160 974 Thermotogae 9 17037 684 Wu D, Jospin G, Eisen JA (2013) Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. PLoS ONE 8(10): e77033. doi:10.1371/journal.pone.0077033
  72. 72. Resources and Reference Data Phylogenomic Methods & Tools Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  73. 73. Resources and Reference Data Phylogenomic Methods & Tools Key Lessons Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  74. 74. Lesson 1 Microbiome-host interactions are way way way way way way way way way way way more complicated than single host- microbe interactions
  75. 75. Eisen Lab “Topics” Phylogenomic Methods & Tools Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  76. 76. The Rise of the Microbiome (2016) 0 500 1000 1500 2000 2500 3000 3500 4000 4500 1 9 5 6 1 9 5 8 1 9 6 1 1 9 6 3 1 9 6 4 1 9 6 5 1 9 6 6 1 9 6 7 1 9 6 8 1 9 6 9 1 9 7 0 1 9 7 1 1 9 7 2 1 9 7 4 1 9 7 5 1 9 7 6 1 9 7 7 1 9 7 8 1 9 7 9 1 9 8 0 1 9 8 1 1 9 8 2 1 9 8 3 1 9 8 4 1 9 8 5 1 9 8 6 1 9 8 7 1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 Pubmed Hits to Microbiome vs. Year
  77. 77. The Rise of the Microbiome Downsides
  78. 78. Microbiomania vs. Germophobia Germophobia Microbiomania
  79. 79. Microbiomania vs. Germophobia Germophobia Microbiomania All Microbes Are Bad Use Antimicrobials in Everything Avoid all Microbes All Microbes Are Good Use Probiotics in Everything Embraces all Microbes Lick Subway Poles Fecal Transplants Will Save World Avoid Animals Too Swab Stories
  80. 80. Microbiomania vs. Germophobia Underselling Overselling All Microbes Are Bad Use Antimicrobials in Everything Avoid all Microbes All Microbes Are Good Use Probiotics in Everything Embraces all Microbes Lick Subway Poles Fecal Transplants Will Save World Avoid Animals Too Swab Stories
  81. 81. Overselling 1: Correlations Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Lesson: Some microbiome correlations with health states are due to microbiomes playing a causal role in health state. But most are not due to causal connections.
  82. 82. Autism - Microbiome - Diet •
  83. 83. Overselling 2: Contamination Lesson: Some “observations” of microbes being present in a system are mistakes
  84. 84. Placenta Microbiome?
  85. 85. Overselling 3: Presence vs. Importance Lesson: Even when microbes are actually present somewhere, this does not mean they are important
  86. 86. Overselling 4: Non pathogen ≠ probiotic https://phylogenomics.blogspot.com/2013/12/cvs-marketing-probiotics-for-everyone.html?spref=tw Lesson: Some probiotics really work, but you can’t just throw a non pathogenic microbe at something and call it a probiotic
  87. 87. Probiotics That Kill … https://phylogenomics.blogspot.com/2012/07/quick-post-story-about-ucdavis.html
  88. 88. Overselling 5: Personalized ≠ Health Lesson: Most claims of personalized microbiome health and diet plans are bogus
  89. 89. Overselling 6: Some Microbes Are Bad Lesson: Hygiene hypothesis is important but imbibing all the microbes in the world is not a good plan
  90. 90. Other Overselling Issues • Big number systems lead to spurious associations • Massive complexity • Just because fecal transplants work for C.diff does not mean they should work for everything
  91. 91. Underselling 1: Kill Everything Lesson: We have gone completely bonkers with overuse of sterilization and antimicrobials
  92. 92. Underselling 2: Swab Stories Lesson: Germaphobia leads to crazy behaviors and great underselling of the possible benefits of microbes
  93. 93. Other Underselling Issues • Related to a pathogen does not mean pathogenic • Microbes with subtle effects have been ignored in most systems (i.e., if they are not pathogens or obligate mutualists) • Microbiomes ignored in many experimental studies of plants and animals • Microbes ignored in most conservation studies
  94. 94. Solution 1: Complain
  95. 95. Solution 1: Complain a lot See http://microbiomania.net
  96. 96. Many others complaining too
  97. 97. http://microBE.net http://gut-check.net Solution 2: Education & Outreach
  98. 98. Kitty Microbiome Georgia Barguil Jack Gilbert Project MERCCURI Phone and Shoes tinyurl/kittybiome Holly Ganz David Coil Solution 3: Citizen Science
  99. 99. Solution 4: Engage Students Too
  100. 100. Microbiomania vs. Germophobia Underselling Overselling All Microbes Are Bad Use Antimicrobials in Everything Avoid all Microbes All Microbes Are Good Use Probiotics in Everything Embraces all Microbes Lick Subway Poles Fecal Transplants Will Save World Avoid Animals Too Swab Stories
  101. 101. Microbiomania vs. Germophobia Underselling Overselling All Microbes Are Bad Use Antimicrobials in Everything Avoid all Microbes All Microbes Are Good Use Probiotics in Everything Embraces all Microbes Lick Subway Poles Fecal Transplants Will Save World Avoid Animals Too Swab Stories
  102. 102. Balance? Goal: Evolve microbiome related communications to be balanced, even though most microbiomes are not
  103. 103. Eisen Lab • Rules

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